OATS is an Oracle Application Testing Suite which provides the easiest and fastest way to automate functional, regression and load testing for Oracle Applications such as E-Business Suite, JDE, Seibel and Oracle Fusion applications (Cloud) as well Web applications and Web services.

Oracle OATS is an Oracle’s recommended toolset that works better with Oracle Applications Testing Automation. Mainly it is:

1. Integrated: It is a one-stop solution for end to end automation and executes the functional, regression, load and performance testing.
2. Extensible & Standards-based: Built on standard Eclipse IDE and using Java provides flexibility for users.
3. User-specific GUI: For technical as well as for normal testers.
4. Efficient Debug: There are multiple panes to debug the code.

Oracle Application Testing Suite (OATS), is a package of three components each of which provides support for different needs.

Oracle Functional Testing (OFT): Oracle Functional Testing (OFT) provides support for functional automated testing on Oracle Technology-based applications.

Oracle Load Testing (OLT): Oracle Load Testing (OLT) provides support for load or performance testing on Oracle Technology-based applications.

Oracle Test Manager (OTM): Oracle Test Manager (OTM) provides support for conducting all test process-related activities like test requirements management, manual and automation test execution.

Oracle Application Testing Suite has the following system requirements:

• Operating System (32-bit and 64-bit versions): Windows 10, Windows 8.1, Windows 7, Windows 2008, Windows 2008 R2, Windows 2012 Server.
• System: x86, 32-bit or 64-bit processor, 2.6 GHz or faster.
• Browser (32-bit): Internet Explorer 11.x; Firefox (ESR) 31.x, 38.x, 45.x; Chrome 33 or higher (32-bit and 64-bit).
• Java Runtime Environment: JRE 1.6 minimum, JRE 1.7, JRE 1.8.
• Application Server: Oracle Fusion Middleware 12.1.3.0.0.

Absolutely. If your application is of web-based, then OATS will support it.

Generally, it takes a year or two. But for particular module automation like Financials, it usually takes 1 year to develop the framework and for automation, etc., According to our methodology, it just completes in only 8-10 weeks.

Yes, we can capture the screenshots and even you can download/export the result into excel format.  can maintain the results history for further analysis.

Below are the few benefits of using OATS:

1. Productivity Improvement and Cost Savings
   • Reduce implementation, upgrade, patch and release testing cycle time
   • Reduce regression testing cycle time
   • Reduce resource requirements for testing
2. Risk Mitigation
   • Avoid failures or system outage due to improper or inadequate testing
   • Quicker identification of the effect of patches in all related areas
   • Quicker identification of changed functionality in a new release
3. Quality of Testing:
   • End to end visibility of testing processes from test requirements through Test results
   • Quicker escalation and defect Reporting
   • Quicker analysis of Test Results.
4. Reliability
   • Pre-built test sets with dependent test cases
   • Consistent testing with pre-seeded test data.

Data Science is an interdisciplinary branch of science that utilizes various algorithms and processes to create business value out of data. It is different from other sciences as it is a blend of many fields, viz. mathematics, statistics, computer science, and information systems.

Artificial Intelligence is the quality displayed by machines, where they acquire human-like cognitive functions like “learning” and “problem-solving”. These acquired capabilities of computer programs help us to better understand and predict the behavior of various industrial process and machinery.

The term Machine Learning (ML) was coined by Arthur Samuel in 1959. Machine Learning is a sub-branch of computer science based on statistics; this field provides the ability to learn from data and later predict the outcomes with accuracy. Machine Learning can be further subdivided into Supervised, Unsupervised and Reinforced learning.

previously latent, like hidden bottlenecks and unprofitable production lines. The three major usage areas of the advanced analytics are:

• Creation of Intelligent Systems that can auto-fix themselves.
• A better understanding of and effectiveness of machines utilization.
• Predictive analytics can reduce downtime and change over time.

Advanced-Data Analytics aids in improving the operations of discrete and continuous manufacturing facilities by increasing productivity and profitability through the implementation of AI over pain areas and problems. The key areas of focus are:

• Advanced Process Analytics
• Real-Time Performance Optimization
• Predictive Analytics
• Asset Monitoring and Predictive Maintenance
• Remote Process Monitoring

Typically, an AI pilot project takes 4-10 weeks for implementation. However, this time heavily depends on the scope of the project and can take shorter or longer duration for completion.

The usage of Cognitive Analytics on the processes can have significant tangible and intangible benefits. The tangible benefits are cost savings, a decrease of downtime, improved quality, reliability of the products and others. Better real-time monitoring and fault detection, increased safety and remote process monitoring can be some of the intangible benefits. Statistically, most AI projects have an ROI of 6 months – 1 year after implementation.

Generally, historical data of 3 to 5 years is sufficient for developing accurate AI models. This can be subjected to the problems that we are trying to solve. In some cases, lesser data can enough to develop actionable insights.

There is a five-step approach to capture data and deploy solutions.

1. Definition the problem statement.
2. Data acquisition from various sources (DCS, PLC, LIMS, SCADA, ERP etc.).
3. Preprocessing of the data and feature selection.
4. Model development and verification.
5. Implementation of solutions through insights, dashboards, and reports.

No, the implementation of these projects does not interrupt with any existing working process and is done in a minimal intervention manner. Most of the projects are done offshore or in a digital image, thus, avoids any disruptions.